Delete arules.py

build/lib/pycaret/arules.py

-# Module: Association Rules Mining
-# Author: Moez Ali <moez.ali@queensu.ca>
-# License: MIT
-# Release: PyCaret 2.0x
-# Last modified : 30/07/2020
-
-def setup(data, 
-          transaction_id, 
-          item_id,
-          ignore_items = None,
-          session_id = None):
-    
-    """
-      
-    Description:
-    ------------
-    This function initializes the environment in pycaret. setup() must called before
-    executing any other function in pycaret. It takes three mandatory parameters:
-    (i) dataframe {array-like, sparse matrix}, (ii) transaction_id param identifying 
-    basket and (iii) item_id param used to create rules. These three params are 
-    normally found in any transactional dataset. pycaret will internally convert the
-    dataframe into a sparse matrix which is required for association rules mining.
-    
-        Example
-        -------
-        from pycaret.datasets import get_data
-        france get_data('france')
-        
-        experiment_name = setup(data = data, transaction_id = 'InvoiceNo', 
-                                item_id = 'ProductName')
-        
-    Parameters
-    ----------
-    data : {array-like, sparse matrix}, shape (n_samples, n_features) where n_samples 
-    is the number of samples and n_features is the number of features.
-
-    transaction_id: string
-    Name of column representing transaction id. This will be used to pivot the matrix.
-
-    item_id: string
-    Name of column used for creation of rules. Normally, this will be the variable of
-    interest.
-    
-    ignore_items: list, default = None
-    list of strings to be ignored when considering rule mining.
-
-    session_id: int, default = None
-    If None, a random seed is generated and returned in the Information grid. The 
-    unique number is then distributed as a seed in all functions used during the 
-    experiment. This can be used for later reproducibility of the entire experiment.
-
-    Returns:
-    --------
-
-    info grid:    Information grid is printed.
-    -----------      
-
-    environment:  This function returns various outputs that are stored in variable
-    -----------   as tuple. They are used by other functions in pycaret.
-    
-    
-    """
-   
-    #exception checking   
-    import sys
-    
-    #ignore warnings
-    import warnings
-    warnings.filterwarnings('ignore') 
-    
-    #load dependencies
-    import random
-    import pandas as pd
-    import numpy as np
-    from IPython.display import display, HTML, clear_output, update_display
-    
-    global X, txid, iid, ignore_list, seed, experiment__
-    
-    #create an empty list for pickling later.
-    experiment__ = []
-    
-    #storing items in variable
-    X = data
-    txid = transaction_id
-    iid = item_id
-    ignore_list = ignore_items
-  
-    #generate seed to be used globally
-    if session_id is None:
-        seed = random.randint(150,9000)
-    else:
-        seed = session_id
-     
-    #display info grid
-    
-    #transactions
-    
-    tx_unique = len(data[transaction_id].unique()) 
-    item_unique = len(data[item_id].unique()) 
-    if ignore_items is None:
-        ignore_flag = 'None'
-    else:
-        ignore_flag = ignore_items 
-    
-    functions = pd.DataFrame ( [ ['session_id', seed ],
-                                 ['# Transactions', tx_unique ], 
-                                 ['# Items', item_unique ],
-                                 ['Ignore Items', ignore_flag ],
-                               ], columns = ['Description', 'Value'] )
-
-    functions_ = functions.style.hide_index()
-    display(functions_)
-    
-    return X, txid, iid, ignore_list, seed, experiment__
-
-def create_model(metric='confidence',
-                 threshold = 0.5,
-                 min_support = 0.05,
-                 round = 4):
-    
-    """  
-     
-    Description:
-    ------------
-    This function creates an association rules model using data and identifiers 
-    passed at setup stage. This function internally transforms the data for 
-    association rule mining.
-
-    setup() function must be called before using create_model()
-
-        Example
-        -------
-        from pycaret.datasets import get_data
-        france get_data('france')        
-        experiment_name = setup(data = data, transaction_id = 'InvoiceNo', 
-                                item_id = 'ProductName')
-
-        This will return dataframe containing rules sorted by metric param. 
-
-    Parameters
-    ----------
-    metric : string, default = 'confidence'
-    Metric to evaluate if a rule is of interest. Default is set to confidence. 
-    Other available metrics include 'support', 'lift', 'leverage', 'conviction'. 
-    These metrics are computed as follows:
-
-    - support(A->C) = support(A+C) [aka 'support'], range: [0, 1]
-
-    - confidence(A->C) = support(A+C) / support(A), range: [0, 1]
-
-    - lift(A->C) = confidence(A->C) / support(C), range: [0, inf]
-
-    - leverage(A->C) = support(A->C) - support(A)*support(C),
-      range: [-1, 1]
-
-    - conviction = [1 - support(C)] / [1 - confidence(A->C)],
-      range: [0, inf]
-    
-    threshold : float, default = 0.5
-    Minimal threshold for the evaluation metric, via the `metric` parameter,
-    to decide whether a candidate rule is of interest.
-    
-    min_support : float, default = 0.05
-    A float between 0 and 1 for minumum support of the itemsets returned.
-    The support is computed as the fraction `transactions_where_item(s)_occur /
-    total_transactions`.
-    
-    round: integer, default = 4
-    Number of decimal places metrics in score grid will be rounded to. 
-
-
-    Returns:
-    --------
-
-    DataFrame:   Dataframe containing rules of interest with all metrics
-    ---------    including antecedents, consequents, antecedent support,
-                 consequent support, support, confidence, lift, leverage,
-                 conviction.
-
-    Warnings:
-    ---------
-
-    - Setting low values for min_support may increase training time.
-  
-     
-    """
-        
-    
-    #loading dependencies
-    import pandas as pd
-    from IPython.display import display, HTML, clear_output, update_display
-    from mlxtend.frequent_patterns import apriori
-    from mlxtend.frequent_patterns import association_rules
-    
-    #reshaping the dataframe
-    basket = X.groupby([txid, iid])[iid].count().unstack().reset_index().fillna(0).set_index(txid)
-    if ignore_list is not None:
-        basket = basket.drop(ignore_list, axis=1)  
-    
-    def encode_units(x):
-        
-        if x <= 0:
-            return 0
-        if x >= 1:
-            return 1
-
-    basket = basket.applymap(encode_units)
-    
-    frequent_itemsets = apriori(basket, min_support=min_support, use_colnames=True)
-    rules = association_rules(frequent_itemsets, metric=metric, min_threshold=threshold)
-    rules = rules.sort_values(by=[metric],ascending=False).reset_index(drop=True)
-    rules = rules.round(round)
-    
-    #storing into experiment
-    tup = ('Basket',basket)
-    experiment__.append(tup)
-    
-    tup = ('Frequent Itemsets',frequent_itemsets)
-    experiment__.append(tup)
-
-    tup = ('Rules',rules)
-    experiment__.append(tup)
-    
-    
-    return(rules)
-
-def plot_model(model,
-               plot = '2d'):
-    
-    """
-      
-    Description:
-    ------------
-    This function takes a model dataframe returned by create_model() function. 
-    '2d' and '3d' plots are available.
-
-        Example:
-        --------
-        
-        rule1 = create_model(metric='confidence', threshold=0.7, min_support=0.05)
-        plot_model(rule1, plot='2d')
-        plot_model(rule1, plot='3d')
-
-    Parameters
-    ----------
-
-    model : DataFrame, default = none
-    DataFrame returned by trained model using create_model(). 
-
-    plot : string, default = '2d'
-    Enter abbreviation of type of plot. The current list of plots supported are:
-
-    Name                                 Abbreviated String     
-    ---------                            ------------------     
-    Support, Confidence and Lift (2d)    '2d'
-    Support, Confidence and Lift (3d)    '3d'
-  
-    
-    Returns:
-    --------
-
-    Visual Plot:  Prints the visual plot. 
-    ------------
-    
-    """
-    
-    #loading libraries
-    import numpy as np
-    import pandas as pd
-    import plotly.express as px
-    from IPython.display import display, HTML, clear_output, update_display
-        
-    #import cufflinks
-    import cufflinks as cf
-    cf.go_offline()
-    cf.set_config_file(offline=False, world_readable=True)
-    
-    #copy dataframe
-    data_ = model.copy()
-    
-    antecedents = []
-    for i in data_['antecedents']:
-        i = str(i)
-        a = i.split(sep="'")
-        a = a[1]
-        antecedents.append(a)
-
-    data_['antecedents'] = antecedents
-
-    antecedents_short = []
-
-    for i in antecedents:
-        a = i[:10]
-        antecedents_short.append(a)
-
-    data_['antecedents_short'] = antecedents_short
-
-    consequents = []
-    for i in data_['consequents']:
-        i = str(i)
-        a = i.split(sep="'")
-        a = a[1]
-        consequents.append(a)
-
-    data_['consequents'] = consequents
-        
-    if plot == '2d':
-
-        fig = px.scatter(data_, x="support", y="confidence", text="antecedents_short", log_x=True, size_max=600, color='lift', 
-                         hover_data = ['antecedents', 'consequents'], opacity=0.5, )
-
-        fig.update_traces(textposition='top center')
-        fig.update_layout(plot_bgcolor='rgb(240,240,240)')
-
-        fig.update_layout(
-            height=800,
-            title_text='2D Plot of Support, Confidence and Lift'
-        )
-
-        fig.show()
-        
-        
-    if plot == '3d':
-        
-        fig = px.scatter_3d(data_, x='support', y='confidence', z='lift',
-                      color='antecedent support', title='3d Plot for Rule Mining', opacity=0.7, width=900, height=800,
-                           hover_data = ['antecedents', 'consequents' ])
-        fig.show()   
-
-def get_rules(data, 
-              transaction_id, 
-              item_id,
-              ignore_items = None,
-              metric='confidence',
-              threshold = 0.5,
-              min_support = 0.05):
-    
-    """
-    Magic function to get Association Rules in Power Query / Power BI.    
-    
-    """
-    
-    s = setup(data=data, transaction_id=transaction_id, item_id=item_id, ignore_items = ignore_items)
-    dataset = create_model(metric=metric, threshold=threshold, min_support=min_support, round=4)
-    
-    return dataset
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